Search Results (6,424)

Background/Objectives: Outpatient stroke rehabilitation is expanding as inpatient episodes shorten. Virtual reality (VR) exergaming can extend practice and standardize progression, but setting-specific effectiveness and implementation factors remain unclear. This scoping review mapped VR exergaming in outpatient stroke care and identified technology typologies and functional outcomes. Methods: Guided by the JBI Manual and PRISMA-ScR, searches of MEDLINE, Embase, CENTRAL, Scopus, and Web of Science were conducted in April 2025. The study included adults post-stroke undergoing VR exergaming programs with movement tracking delivered in clinic-based outpatient or home-based outpatient settings. Interventions focused on functional rehabilitation using interactive VR. Results: Sixty-six studies met the criteria, forty-four clinic-based and twenty-two home-based. Serious games accounted for 65% of interventions and commercial exergames for 35%. Superiority on a prespecified functional endpoint was reported in 41% of trials, 29% showed within-group improvement only, and 30% found no between-group difference; effects were more consistent in supervised clinic programs than in home-based implementations. Signals were most consistent for commercial off-the-shelf and camera-based systems. Gloves or haptics and locomotor platforms were promising but less studied. Head-mounted display interventions showed mixed findings. Adherence was generally high, and adverse events were infrequent and mild. Conclusions: VR exergaming appears clinically viable for outpatient stroke rehabilitation, with the most consistent gains in supervised clinic-based programs; home-based effects are more variable and sensitive to dose and supervision. Future work should compare platform types by therapeutic goal; embed mechanistic measures; strengthen home delivery with dose control and remote supervision; and standardize the reporting of fidelity, adherence, and cost. Full article

This study explores patients’ experiences participating in a home-based remote patient monitoring program for chronic disease management. Using a mixed-methods approach, data was collected through semi-structured interviews and surveys from participants with Chronic Obstructive Pulmonary Disease (COPD) and diabetes. Two key themes emerged: “knowing” and “relationship.” The “knowing” theme encompassed data-driven awareness and contextualized education that empowered patients in their health management. The “relationship” theme highlighted the importance of interpersonal connections with healthcare providers and the sense of security from clinical oversight. Technology served as a communication platform supporting patient-clinician interactions rather than replacing them. The findings demonstrate that remote monitoring programs enhance chronic disease self-management through two interconnected mechanisms: the development of ‘situated health literacy’ through real-time, personalized data interpretation, and strengthened therapeutic relationships enabled by technology-mediated clinical oversight. Rather than replacing human interaction, technology serves as a platform for meaningful patient-provider communication that supports both immediate health management and long-term self-management capability development. These exploratory findings suggest potential design considerations for patient-centered telehealth services that integrate health literacy enhancement with relationship-centered care. Full article

Artificial intelligence (AI) is transforming cardiac electrophysiology across the entire care pathway, from arrhythmia detection on 12-lead electrocardiograms (ECGs) and wearables to the guidance of catheter ablation procedures, through to outcome prediction and therapeutic personalization. End-to-end deep learning (DL) models have achieved cardiologist-level performance in rhythm classification and prognostic estimation on standard ECGs, with a reported arrhythmia classification accuracy of ≥95% and an atrial fibrillation detection sensitivity/specificity of ≥96%. The application of AI to wearable devices enables population-scale screening and digital triage pathways. In the electrophysiology (EP) laboratory, AI standardizes the interpretation of intracardiac electrograms (EGMs) and supports target selection, and machine learning (ML)-guided strategies have improved ablation outcomes. In patients with cardiac implantable electronic devices (CIEDs), remote monitoring feeds multiparametric models capable of anticipating heart-failure decompensation and arrhythmic risk. This review outlines the principal modeling paradigms of supervised learning (regression models, support vector machines, neural networks, and random forests) and unsupervised learning (clustering, dimensionality reduction, association rule learning) and examines emerging technologies in electrophysiology (digital twins, physics-informed neural networks, DL for imaging, graph neural networks, and on-device AI). However, major challenges remain for clinical translation, including an external validation rate below 30% and workflow integration below 20%, which represent core obstacles to real-world adoption. A joint clinical engineering roadmap is essential to translate prototypes into reliable, bedside tools. Full article

Background: Hereditary spastic paraplegias (HSPs) are a group of neurodegenerative disorders marked by progressive corticospinal tract dysfunction and wide phenotypic variability. Their genetic heterogeneity has so far limited the identification of biomarkers that are broadly applicable across different subtypes. Objective: We aim to define a balanced review on the use of biomarkers in HSP. Methods: This review focuses on fluid biomarkers already available in clinical or research settings—primarily validated in other neurodegenerative diseases—and assesses their potential translation to the HSP context. Biomarkers such as neurofilament light chain, brain-derived tau, glial fibrillary acidic protein, and soluble TREM2 reflect key converging mechanisms of neurodegeneration, including axonal damage, neuronal loss, and glial activation. These shared downstream pathways represent promising targets for disease monitoring in HSP, independently of the underlying genetic mutation. Results: An integrative framework of fluid biomarkers could assist in defining disease progression and stratify patients in both clinical and research settings. Moreover, recent advances in ultrasensitive assays and remote sampling technologies, such as dried blood spot collection, offer concrete opportunities for minimally invasive, longitudinal monitoring. When combined with harmonized multicenter protocols and digital infrastructure, these tools could support scalable and patient-centered models of care. Conclusions: The integration of already available biomarkers into the HSP field may accelerate clinical translation and offer a feasible strategy to overcome the challenges posed by genetic and clinical heterogeneity. Full article

Accurate recognition of basic facial emotions is well documented, yet the mechanisms of misclassification and their relation to gaze allocation remain under-reported. The present study utilized a within-subjects eye-tracking design to examine both accurate and inaccurate recognition of five basic emotions (anger, disgust, fear, happiness, and sadness) in healthy young adults. Fifty participants (twenty-four women) completed a forced-choice categorization task with 10 stimuli (female/male poser × emotion). A remote eye tracker (60 Hz) recorded fixations mapped to eyes, nose, and mouth areas of interest (AOIs). The analyses combined accuracy and decision-time statistics with heatmap comparisons of misclassified versus accurate trials within the same image. Overall accuracy was 87.8% (439/500). Misclassification patterns depended on the target emotion, but not on participant gender. Fear male was most often misclassified (typically as disgust), and sadness female was frequently labeled as fear or disgust; disgust was the most incorrectly attributed response. For accurate trials, decision time showed main effects of emotion (p < 0.001) and participant gender (p = 0.033): happiness was categorized fastest and anger slowest, and women responded faster overall, with particularly fast response times for sadness. The AOI results revealed strong main effects and an AOI × emotion interaction (p < 0.001): eyes received the most fixations, but fear drew relatively more mouth sampling and sadness more nose sampling. Crucially, heatmaps showed an upper-face bias (eye AOI) in inaccurate trials, whereas accurate trials retained eye sampling and added nose and mouth AOI coverage, which aligned with diagnostic cues. These findings indicate that the scanpath strategy, in addition to information availability, underpins success and failure in basic-emotion recognition, with implications for theory, targeted training, and affective technologies. Full article

Under the combined effects of climate change, overexploitation, and intense grazing, temperate steppe in northern China is experiencing increasing deterioration, which is typified by a shift from structural degradation to functional disruption. Accurately tracking steppe degradation using remote sensing technology has emerged as a crucial scientific concern. Prior research failed to integrate ecosystem structure and function and lacked reference baselines, relying only on individual indicators to quantify degradation. To resolve these gaps, this study established a novel degradation evaluation index system integrating ecosystem structure and function, incorporating vegetation community distribution and proportions of degradation-indicator species to define reference states and quantify degradation severity. Analyzed spatiotemporal evolution and drivers across the temperate typical steppe (2013–2022). Key findings reveal (1) non-degraded and slightly degraded areas dominated (75.57% mean coverage), showing an overall fluctuating improvement trend; (2) minimal transitions between degradation levels, with stable conditions prevailing (59.52% unchanged area), indicating progressive degradation reversal; and (3) natural factors predominated as degradation drivers. The integrated structural–functional framework enables more sensitive detection of early degradation signals, thereby informing more effective steppe restoration management. Full article

Information and communication technology (ICT) infrastructure can facilitate urban–rural integration. However, few studies have explored the role of virtual agglomeration in the mechanisms underlying this process, which can enable geographically dispersed market participants (both urban and rural) to achieve proximity in network space through digital connectivity provided by ICT. This study uses the PLS-SEM method to empirically analyzes the relationships among ICT infrastructure, virtual agglomeration, and urban–rural integration based on data obtained from 31 provincial-level regions in China from 2012 to 2022. The results indicate that: (1) ICT infrastructure can promote urban–rural integration. (2) Virtual agglomeration plays a significant mediating role in the relationship between ICT infrastructure and urban–rural integration. In relatively developed eastern China, virtual agglomeration fully mediates the impact of ICT infrastructure on urban–rural integration. (3) Other complementary infrastructures—including transport and education—have positive moderating effects on the process of virtual agglomeration facilitated by ICT. This study advances the understanding of ICT’s effects on regional development from the perspective of employing a new form of spatial agglomeration (i.e., virtual agglomeration). Meanwhile, this study indicates that in order to address the global challenge of urban–rural divide, it is necessary to strengthen the development of ICT infrastructure in remote rural areas, while developing complementary infrastructure such as transportation or education in alignment with regional characteristics. Full article

Underwater wireless optical communication (UWOC) is a key technology in ocean resource development, and its link stability is often limited by the difficulty of optical alignment in complex underwater environments. In response to this difficulty, this study has focused on improving the Real-Time Detection Transformer v2 (RT-DETRv2) model. We have improved the underwater light source detection model by collaboratively designing a lightweight backbone network and deformable convolution, constructing a cross-stage local attention mechanism to reduce the number of network parameters, and introducing geometrically adaptive convolution kernels that dynamically adjust the distribution of sampling points, enhance the representation of spot-deformation features, and improve positioning accuracy under optical interference. To verify the effectiveness of the model, we have constructed an underwater light-emitting diode (LED) light-spot detection dataset containing 11,390 images was constructed, covering a transmission distance of 15–40 m, a ±45° deflection angle, and three different light-intensity conditions (noon, evening, and late night). Experiments show that the improved model achieves an average precision at an intersection-over-union threshold of 0.50 (AP50) value of 97.4% on the test set, which is 12.7% higher than the benchmark model. The UWOC system built based on the improved model achieves zero-bit-error-rate communication within a distance of 30 m after assisted alignment (an initial lateral offset angle of 0°–60°), and the bit-error rate remains stable in the 10⁻⁷–10⁻⁶ range at a distance of 40 m, which is three orders of magnitude lower than the traditional Remotely Operated Vehicle (ROV) underwater optical communication system (a bit-error rate of 10⁻⁶–10⁻³), verifying the strong adaptability of the improved model to complex underwater environments. Full article

The development of high-speed computers and electronic memories, high-frequency communication networks, electroluminescent and photovoltaic devices, flexible displays, and more requires new materials with unique properties, such as a low dielectric constant, an adjustable refractive index, high hardness, thermal resistance, and processability. SiOC coatings possess a number of desirable properties required by modern technologies, including good heat and UV resistance, transparency, high electrical insulation, flexibility, and solubility in commonly used organic solvents. Chemical vapor deposition (CVD) is a very useful and convenient method to produce this type of layer. In this article we present the results of studies on SiOC coatings obtained from tetramethylcyclotetrasiloxane in a remote hydrogen plasma CVD process. The elemental composition (XPS, EDS) and chemical structure (FTIR and NMR spectroscopy-¹³C, ²⁹Si) of the obtained coatings were investigated. Photoluminescence analyses and ellipsometric and thermogravimetric measurements were also performed. The surface morphology was characterized using AFM and SEM. The obtained results allowed us to propose a mechanism for the initiation and growth of the SiOC layer. Full article

Background: Telemedicine has gained significance, especially during the COVID-19 pandemic, offering remote healthcare solutions. However, its adoption in geriatric medical centers (GMCs) remains limited. Understanding the factors influencing telemedicine acceptance among care teams in geriatric medical centers is crucial for successful implementation. Aim: This study examines behavioral factors influencing telemedicine adoption among care teams in Israeli geriatric medical centers through the lens of the Technology Acceptance Model. Methods: A cross-sectional study was conducted with 406 healthcare professionals from four geriatric medical centers in Israel. Participants completed a self-administered questionnaire measuring self-efficacy, subjective norms, anxiety, resistance to change, perceived usefulness, perceived ease of use, and intention to use telemedicine. Structural equation modeling was used to analyze the data. Results: Perceived ease of use mediated the associations between self-efficacy and perceived usefulness and between subjective norms and perceived usefulness, demonstrating how confidence shapes technology acceptance. Perceived usefulness mediated the association between perceived ease of use and intention to use. Perceived ease of use did not mediate the relationship between anxiety or resistance to technological changes and perceived usefulness. Conclusions: The study highlights the importance of perceived ease of use and usefulness in promoting telemedicine adoption among geriatric medical center care teams, emphasizing the need for targeted interventions to enhance these perceptions. Full article

(1) Background: Technological advancements provide new opportunities to objectively assess motor deficits in people with Multiple Sclerosis (PwMS). This pilot study aimed to evaluate the performance and usability of iFeel, a novel wearable system which integrates inertial sensors, instrumented shoes, and an AI-based algorithm. (2) Methods: Sixteen adult PwMS (Expanded Disability Status Scale—EDSS ≤ 6) performed motor tests (Timed 25-Foot Walk—T25FW; Timed Up and Go—TUG) both with and without the iFeel suit. Patient-reported outcomes (PROs) were also collected to assess perceived fatigue, dual-task impact, and walking difficulties. System Usability Scale (SUS) and ad hoc questionnaires have been further administered to test usability. (3) Results: No significant differences were found between the clinician and system-based scores for both T25FW (p = 0.383) and TUG (p = 0.447). Reliability analyses showed good agreement for T25FW (Intraclass Correlation Coefficient—ICC = 0.83) and excellent agreement for TUG (ICC = 0.92). Sensor-derived measures correlated strongly with PROs on fatigue, dual-task interference, and mobility. Usability was rated high (SUS: 78.6 ± 16.1), with participants reporting minimal discomfort and positive perceptions of iFeel usefulness for rehabilitation, health monitoring, and daily activities. (4) Conclusions: This pilot study provides preliminary yet promising evidence on the feasibility, usability, and perceived usefulness of the iFeel technology for motor assessment in PwMS. The findings support its further development and potential integration into clinical practice, particularly for remote or continuous motor monitoring. Full article

The responsibility for risk assessment and user safety in forested and recreational areas lies with the property owner. This study shows that unmanned aerial vehicles (UAVs), combined with remote sensing and GIS analysis, effectively support the identification of high-risk trees, particularly those with reduced structural stability. UAV-based surveys successfully detect 78% of dead or declining trees identified during ground inspections, while significantly reducing labor and enabling large-area assessments within a short timeframe. The study covered an area of 6.69 ha with 51 reference trees assessed on the ground. Although the multispectral camera also recorded the red-edge band, it was not included in the present analysis. Compared to traditional ground-based surveys, the UAV-based approach reduced fieldwork time by approx. 20–30% and labor costs by approx. 15–20%. Orthomosaics generated from images captured by commercial multispectral drones (e.g., DJI Mavic 3 Multispectral) provide essential information on tree condition, especially mortality indicators. UAV data collection is fast and relatively low-cost but requires equipment capable of capturing high-resolution imagery in specific spectral bands, particularly near-infrared (NIR). The findings suggest that UAV-based monitoring can enhance the efficiency of large-scale inspections. However, ground-based verification remains necessary in high-traffic areas where safety is critical. Integrating UAV technologies with GIS supports the development of risk management strategies aligned with the principles of precision forestry, enabling sustainable, more proactive and efficient monitoring of tree-related hazards. Full article

The evolution of decentralized clinical trials, driven by advanced digital technologies, is transforming traditional clinical research. It introduces innovative methods for informed consent, remote patient monitoring, and data analysis, enhancing study efficiency, validity, and participation while reducing patient burden. Some clinical procedures can be conducted remotely, increasing trial accessibility and reducing population selection biases, particularly for cardiovascular patients. However, this also presents complex regulatory and ethical challenges. The article explores how digital platforms and emerging technologies like block chain, AI, and advanced cryptography can promote traceability, security, and transparency throughout the trial process, ensuring participant identification and documentation of each procedural step. Clear, legally compliant informed consent, often managed through electronic systems, both for research participation and data management in line with GDPR, is essential. Ethical considerations include ensuring participants understand trial information, with adaptations such as simplified language, visual aids, and multilingual support. The transnational nature of decentralized trials highlights the need for coordinated regulatory standards to overcome jurisdictional barriers and reinforce accountability. This framework promotes trust, shared responsibility, and the protection of participants rights while upholding high ethical standards in scientific research. Full article

Real-time, accurate and detailed monitoring of urban green space is of great significance for constructing the urban ecological environment and maximizing ecological benefits. Although high-resolution remote sensing technology provides rich ground object information, it also makes the surface information of urban green spaces more complex. Existing classification methods often struggle to meet the requirements of classification accuracy and the automation demands of high-resolution images. This study utilized GF-7 remote sensing imagery to construct an urban green space classification method for Beijing. The study used the YOLO v8 model as the framework to conduct a fine classification of urban green spaces within the Fifth Ring Road of Beijing, distinguishing between evergreen trees, deciduous trees, shrubs and grasslands. The aims were to address the limitations of insufficient model fit and coarse-grained classifications in existing studies, and to improve vegetation extraction accuracy for green spaces in northern temperate cities (with Beijing as a typical example). The results show that the overall classification accuracy of the trained YOLO v8 model is 89.60%, which is 25.3% and 28.8% higher than that of traditional machine learning methods such as Maximum Likelihood and Support Vector Machine, respectively. The model achieved extraction accuracies of 92.92%, 93.40%, 87.67%, and 93.34% for evergreen trees, deciduous trees, shrubs, and grasslands, respectively. This result confirms that the combination of deep learning and high-resolution remote sensing images can effectively enhance the classification extraction of urban green space vegetation, providing technical support and data guarantees for the refined management of green spaces and “garden cities” in megacities such as Beijing. Full article